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8th Workshop on Statistical Machine Translation, WMT 2013

作者： Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284572

We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving towards a more principled phrase generation and probability estimation. During training, we allow the decoder to generate new phrases on-the-fly and increment the maximum phrase length in each iteration. Experiments are carried out on the IWSLT 2011 Arabic-English task, where we are able to reach moderate improvements on a state-of-the-art baseline with our training method. The resulting phrase table shows only a small overlap with the heuristically extracted one, which demonstrates the restrictiveness of limiting phrase selection by a word alignment or heuristics. By interpolating the heuristic and the trained phrase table, we can improve over the baseline by 0.5% BLEU and 0.5% TER. © 2013 Association for Computational Linguistics

关键词： Decoding

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Extended translation models in phrase-based decoding 10

Extended translation models in phrase-based decoding

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10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Guta, Andreas Wuebker, Joern Graça, Miguel Kim, Yunsu Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781941643327

We propose a novel extended translation model (ETM) to counteract some problems in phrase-based translation: The lack of translation context when using singleword phrases and uncaptured dependencies beyond phrase boundaries. The ETM operates on word-level and augments the IBM models by an additional bilingual word pair and a reordering operation. Its implementation in a phrase-based decoder introduces translation and reordering dependencies for single-word phrases and dependencies across phrase boundaries. More, the model incorporates an explicit treatment of multiple and empty alignments. Its integration outperforms competitive systems that include lexical and phrase translation models as well as hierarchical reordering models on 4 language pairs significantly by +0.7% BLEU on average. Although simpler and using fewer dependencies, the ETM proves to be on par with 7-gram operation sequence models (Durrani et al., 2013b). © EMNLP 2015. All rights reserved.

关键词： Decoding

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A comparison between count and neural network models based on joint translation and reordering sequences

A comparison between count and neural network models based o...

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Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Guta, Andreas Alkhouli, Tamer Peter, Jan-Thorsten Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781941643327

We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear sequences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending lexical and alignment dependencies on the word level into a single framework. They are constructed in a simple manner while capturing multiple alignments and empty words. JTR sequences can be used to train a variety of models. We investigate the performances of ngram models with modified Kneser-Ney smoothing, feed-forward and recurrent neural network architectures when estimated on JTR sequences, and compare them to the operation sequence model (Durrani et al., 2013b). Evaluations on the IWSLT German→English, WMT German→English and BOLT Chinese→English tasks show that JTR models improve state-of-the-art phrasebased systems by up to 2.2 BLEU. © 2015 Association for Computational Linguistics.

关键词： Alignment

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Alignment-Based Neural Machine Translation 1

Alignment-Based Neural Machine Translation

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1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Alkhouli, Tamer Bretschner, Gabriel Peter, Jan-Thorsten Hethnawi, Mohammed Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781945626104

Neural machine translation (NMT) has emerged recently as a promising statistical machine translation approach. In NMT, neural networks (NN) are directly used to produce translations, without relying on a pre-existing translation framework. In this work, we take a step towards bridging the gap between conventional word alignment models and NMT. We follow the hidden Markov model (HMM) approach that separates the alignment and lexical models. We propose a neural alignment model and combine it with a lexical neural model in a log-linear framework. The models are used in a standalone word-based decoder that explicitly hypothesizes alignments during search. We demonstrate that our system outperforms attention-based NMT on two tasks: IWSLT 2013 German→English and BOLT Chinese→English. We also show promising results for re-aligning the training data using neural models. © 2016 Association for Computational Linguistics

关键词： Neural machine translation

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Improving statistical machine translation with word class models

Improving statistical machine translation with word class mo...

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2013 Conference on Empirical Methods in Natural language Processing, EMNLP 2013

作者： Wuebker, Joern Peitz, Stephan Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284978

Automatically clustering words from a monolingual or bilingual training corpus into classes is a widely used technique in statistical natural language processing. We present a very simple and easy to implement method for using these word classes to improve translation quality. It can be applied across different machine translation paradigms and with arbitrary types of models. We show its efficacy on a small German → English and a larger French → German translation task with both standard phrase-based and hierarchical phrase-based translation systems for a common set of models. Our results show that with word class models, the baseline can be improved by up to 1.4% BLEU and 1.0% TER on the French → German task and 0.3% BLEU and 1.1% TER on the German → English task. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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Investigations on phrase-based decoding with recurrent neural network language and translation models 10

Investigations on phrase-based decoding with recurrent neura...

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10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Alkhouli, Tamer Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781941643327

This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural models. In this paper, we apply approximations and use caching to enable RNN decoder integration, while requiring reasonable memory and time resources. We analyze the effect of caching on translation quality and speed, and use it to integrate RNN language and translation models into a phrase-based decoder. To the best of our knowledge, no previous work has discussed the integration of RNN translation models into phrase-based decoding. We also show that a special RNN can be integrated efficiently without the need for approximations. We compare decoding using RNNs to rescoring n-best lists on two tasks: IWSLT 2013 German→English, and BOLT Arabic→English. We demonstrate that the performance of decoding with RNNs is at least as good as using them in rescoring. © EMNLP 2015. All rights reserved.

关键词： Recurrent neural networks

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Lexicon Models for Hierarchical Phrase-Based Machine Translation 8

Lexicon Models for Hierarchical Phrase-Based Machine Transla...

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8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Huck, Matthias Mansour, Saab Wiesler, Simon Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

In this paper, we investigate lexicon models for hierarchical phrase-based statistical machine translation. We study five types of lexicon models: a model which is extracted from word-aligned training data and-given the word alignment matrix-relies on pure relative frequencies [1];the IBM model 1 lexicon [2];a regularized version of IBM model 1;a triplet lexicon model variant [3];and a discriminatively trained word lexicon model [4]. We explore source-to-target models with phrase-level as well as sentence-level scoring and target-to-source models with scoring on phrase level only. For the first two types of lexicon models, we compare several scoring variants. All models are used during search, i.e. they are incorporated directly into the log-linear model combination of the decoder. Phrase table smoothing with triplet lexicon models and with discriminative word lexicons are novel contributions. We also propose a new regularization technique for IBM model 1 by means of the Kullback-Leibler divergence with the empirical unigram distribution as regularization term. Experiments are carried out on the large-scale NIST Chinese→English translation task and on the English→French and Arabic→English IWSLT TED tasks. For Chinese→English and English→French, we obtain the best results by using the discriminative word lexicon to smooth our phrase tables. © IWSLT 2011. All rights reserved.

关键词： Computer aided language translation

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Deep fisher faces 28

Deep fisher faces

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28th British Machine Vision Conference, BMVC 2017

作者： Hanselmann, Harald Yan, Shen Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)190172560X

Most current state-of-the-art methods for unconstrained face recognition use deep convolutional neural networks. Recently, it has been proposed to augment the typically used softmax cross-entropy loss by adding a center loss trying to minimize the distance between the face images and their class centers. In this work we further extend the center (intra-class) loss with an inter-class loss reminiscent of the popular early face recognition approach Fisherfaces. To this end we add a term that directly optimizes the distances of the class centers appearing in a batch in dependence of the input images. We evaluate the new loss on two popular databases for unconstrained face recognition, the Labeled Faces in the Wild and the Youtube Faces database. In both cases the new loss achieves competitive results. © 2017. The copyright of this document resides with its authors.

关键词： Face recognition

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Tracking benchmark databases for video-based sign language recognition

Tracking benchmark databases for video-based sign language r...

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11th European Conference on Computer Vision, ECCV 2010

作者： Dreuw, Philippe Forster, Jens Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented. © 2012 Springer-Verlag.

关键词： Database systems

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Spoken language processing techniques for sign language recognition and translation

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Technology and Disability 2008年第2期20卷 121-133页

作者： Dreuw, Philippe Stein, Daniel Deselaers, Thomas Rybach, David Zahedi, Morteza Bungeroth, Jan Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department 6 RWTH Aachen University Aachen Germany

We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results. © 2008 IOS Press. All rights reserved.

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